Category: GPU Server

771 ViewsTo create a Kubernetes master on Google Cloud and add on-premise instances with GPUs as nodes, follow these steps: Step 1: Set Up the Kubernetes Master on Google Cloud 2. Obtain Cluster Credentials: Step 2: Set Up On-Premise Nodes with GPU 2. Install NVIDIA Drivers and GPU Support: 3. Join the On-Premise Nodes to […]

844 ViewsTo enable hardware acceleration on a headless servers, especially for tasks like GPU computing or rendering, you’ll need to follow several steps depending on your use case and the hardware you’re using. Here’s a general guide to setting up hardware acceleration on a headless server: 1. Ensure Hardware Compatibility First, verify that your server’s […]

792 ViewsThe error message “(EE) no screens found (EE)” usually occurs when Xorg, the display server for Linux, cannot find a compatible screen or display device. To fix Xorg in this situation, you may need to adjust the configuration files or install the necessary drivers. In the context of running a GPU instance on Google […]

912 ViewsTo simply extend any 1U or 2U server to contain multiple 2-slot, full-height GPUs especially for sharing, you will have to utilize some dedicated hardware solutions just because of the bounded physical space and power limitation of all these form components. Here is the complete guide and several choices to easily get this:  1. […]

1,037 ViewsTo find out which pods are scheduled on a GPU in a Kubernetes cluster, you can use the following steps: 1. Use kubectl to List Pods with GPU Requests Kubernetes uses resource requests and limits to manage GPU allocation. To identify which pods are scheduled with GPU resources, you can filter pods based on […]

525 ViewsA server usually does not require a GPU. The need for it completely depends on its use and some dedicated apps that a GPU server will easily run. So, here is the complete analysis of some everyday situations: General Computing Tasks: A GPU is not always needed for carrying out some basic tasks, such […]

417 ViewsThe error message “Slurm srun cannot allocate resources for GPUs – Invalid generic resource specification” typically means there’s a problem with how the GPU resources are specified or requested in your Slurm configuration or job script. Here are a few things you can check and try to resolve this issue: Ensure that “tesla” is […]

984 ViewsTo identify a faulty GPU slot in a server running Ubuntu, you can use a combination of system commands and utilities. Here’s a step-by-step guide to help you through the process: 1. Check GPU Information with lspci The lspci command lists all PCI devices, including GPUs. To identify the GPU devices, you can use: […]

807 ViewsTo address the “No capacity” issue programmatically when creating VMs with GPUs in Google Cloud Platform (GCP), you can use the Google Cloud SDK or the GCP API to automate some of the steps I mentioned. Here are a few coding strategies to help you resolve the issue: 1. Check Available Zones for GPUs […]

261 ViewsIf you encounter the issue where Google Cloud Platform (GCP Resource) does not have enough resources available to fulfill your request, and you want to address or mitigate this programmatically, you can use code to handle or work around the resource limitations. Here’s how you might approach resolving or mitigating this issue with code: […]